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An Early Evaluation of Intel’s Optane DC Persistent Memory Module and its Impact on High-Performance Scientific Applications

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Related Edinburgh Organisations

Original languageEnglish
Title of host publicationProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis
Publication statusAccepted/In press - 14 Jun 2019
EventInternational Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2019): SC 2019 - Denver, United States
Duration: 19 Nov 201921 Nov 2019
https://sc19.supercomputing.org/

Conference

ConferenceInternational Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2019)
CountryUnited States
CityDenver
Period19/11/1921/11/19
Internet address

Abstract

Memory and I/O performance bottlenecks in supercomputing sim- ulations are two key challenges that need to be addressed on the road to Exascale. The recently released byte-addressable persistent non-volatile memory technology from Intel, DCPMM, promises to be an exciting opportunity to break with the status quo, with unprecedented levels of capacity at near-DRAM speeds. In this paper, we explore the potential of DCPMM in the context of high- performance scientific computing using two distinct applications in terms of outright performance, efficiency and usability for both its Memory and App Direct modes. In Memory mode, we show that it is possible to achieve equivalent performance and better efficiency for a CASTEP simulation that struggles with memory capacity limitations on conventional DRAM-only systems without needing to introduce any changes to the application. For IFS, we demonstrate that using a distributed object-store over the NVRAM devices reduces the data contention created in weather forecasting data producer-consumer workflows. In addition to presenting the impact on two applications, we also present results for achievable memory bandwidth performance using STREAM.

ID: 101981079